The debate surrounding AI’s role in clinical research is often reduced to the automation of discrete tasks, such as summarizing literature, analysis copilots, and assisting with prose, this "tool-use" paradigm obscures a more fundamental transformation. We propose a shift toward agentic research infrastructure, where AI systems function not as passive instruments, but as active collaborators in the scientific process. Co-authored by a clinical psychology doctoral researcher, a computational psychotherapy scholar, and the AI agent itself, this paper argues that the transition from passive to agentic AI represents a "change in kind" rather than degree. Drawing on a months-long collaboration involving over 30 specialized research capabilities, we demonstrate how agentic systems reconfigure the topology of the research process. By collapsing the temporal friction between theoretical intuition and empirical validation, these systems transform clinical inquiry from a rigid, linear pipeline into a fluid, multidimensional landscape. This newfound immediacy allows clinician-researchers to ask, pursue, and pivot between complex questions in real-time—expanding the investigative horizon to include inquiries previously sidelined by the logistical constraints of traditional methods. We introduce the concept of "Agent Learning" to describe the accumulation of domain-specific nuance through sustained research engagement and argue that formalizing human-agent methodologies is now an urgent priority for the future of clinical psychological inquiry.